{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "In this notebook I'll make the rules which could directly predict almost 43-47%(coverage) of data without any Machine Learning Models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kranthi/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15, 4]\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "\n",
    "pd.options.display.max_rows=1000\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data():\n",
    "    train = pd.read_csv(\"../data/train.csv\")\n",
    "    test = pd.read_csv(\"../data/test.csv\")\n",
    "    sub = pd.read_excel(\"../data/sample_submission.xlsx\")\n",
    "\n",
    "    print(train.shape, test.shape, sub.shape)\n",
    "    \n",
    "    return train, test, sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(543, 8) (233, 7) (233, 2)\n",
      "(776, 8)\n"
     ]
    }
   ],
   "source": [
    "train, test, sub = get_data()\n",
    "\n",
    "data = pd.concat([train, test], axis=0)\n",
    "data['source'] = np.nan\n",
    "data['source'].iloc[:train.shape[0]] = \"train\"\n",
    "data['source'].iloc[train.shape[0]: ] = \"test\"\n",
    "data.drop(['IsUnderRisk'], axis=1, inplace=True)\n",
    "\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Location_Score</th>\n",
       "      <th>Internal_Audit_Score</th>\n",
       "      <th>External_Audit_Score</th>\n",
       "      <th>Fin_Score</th>\n",
       "      <th>Loss_score</th>\n",
       "      <th>Past_Results</th>\n",
       "      <th>IsUnderRisk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>8.032</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>77.730</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>59.203</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>73.080</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15.666</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City  Location_Score  Internal_Audit_Score  External_Audit_Score  \\\n",
       "0     2           8.032                    14                     8   \n",
       "1    31          77.730                     8                     3   \n",
       "2    40          59.203                     3                    12   \n",
       "3    12          73.080                     4                     5   \n",
       "4     4          15.666                    13                    15   \n",
       "\n",
       "   Fin_Score  Loss_score  Past_Results  IsUnderRisk  \n",
       "0          3           6             0            1  \n",
       "1          3           8             1            0  \n",
       "2         11           3             0            1  \n",
       "3          7           6             0            0  \n",
       "4          6           7             2            1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = 'IsUnderRisk'\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                     43\n",
       "Location_Score          540\n",
       "Internal_Audit_Score     13\n",
       "External_Audit_Score     13\n",
       "Fin_Score                13\n",
       "Loss_score               10\n",
       "Past_Results              7\n",
       "IsUnderRisk               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad177c8450>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WXdO+fXvFxMQoPz9fycnJKigoUIcOHRQdHe2uGThwoHx8fJSfn9+qgnvYqgKjWwAANIOj06OMbgEAAJhco4L7FRUVFTp79qwuXrwol6v+gv2gQYMavc25c+cqPDxcUVE//APmyqn5VqvVo85qter8+fOSJIfDoYCAAI8b5lksFnXs2FEOh6PRPQAAAAAAYDaNCu4XL17UnDlztGPHDtXW1tabd7lcslgs+uc//9moJl588UUdPnxYu3fvlpeXl8fc1Xexv7KPhuavVXO14uLiRvUHAEBz4TfJGJ2MbgAA0Cxa6u/qjc4Wb1RwT01N1c6dO/X0009r0KBBTXLn9nnz5ik7O1vvv/++unXr5h632WySflhV79Kli3u8tLTUvQofGBio0tJSj6DucrlUVlZWb6X+x1rkKfR7OVUeAFqjFvmb1ApU3LgEANACtdbf1UYF948++kjTpk3Tf/zHfzTJzufMmaPs7Gzt3LlTvXr18pjr2rWrbDabcnNz9atf/UqSVFNTo7y8PM2fP1+SFBUVpcrKShUUFLivcy8oKFBVVZXHde8AAAAAALRUjQru7dq1U48ePZpkx7NmzdK7776rDRs2yM/Pz31Nu4+Pjzp06CCLxaKUlBQtW7ZMdrtdPXv21NKlS+Xj46NHH31UkhQaGqq4uDilpqYqIyNDLpdLqampeuihh1rt/7QAAAAAAO4sN3yO+48lJSVp7969TbLjdevWqaKiQklJSQoNDXW/3nzzTXfNzJkzNX36dM2ePVsPPvigLly4oOzsbPn6+rpr1q5dq379+mnMmDEaO3as+vXrpzVr1jRJjwAAAAAAGK1Rz3E/efKkJk+erNDQUE2ePFnBwcH1biYn1b8TPG4dj4MDgNaJx8EZg+e4A0DrxHPcJUVGRspisejIkSPaunVrg3WNvas8AAAAAAC4tkYF9xdeeOG6j1kDAAAAAABNq1HBfd68ec3VBwAAAAAAuIZG3ZwOAAAAAADcXo1acV+0aNENaywWi1544YWf3BAAAAAAAPg/jQruCxcubHDOYrHI5XIR3AEAAAAAaEKNCu4XL16sN1ZXV6fTp09rzZo1ys/PV1ZWVpM1BwAAAADAne6Wr3G/66671K1bN6Wnp6tr166aO3duU/QFAAAAAADUxDenGzx4sPbs2dOUmwQAAAAA4I7WpMG9uLhYLperKTcJAAAAAMAdrVHXuH/66afXHC8vL9eBAwe0du1ajRo1qkkaAwAAAAAAjQzuCQkJslgs9cZdLpe8vLw0duzYm3pkHAAAAAAAuDmNCu7vv/9+vTGLxSI/Pz+FhITI19e3yRoDAAAAAACNDO6//vWvm6sPAAAAAABwDY0K7ldUVFTo4MGDOn36tCQpJCREv/71r1lxBwAAAACgiTU6uK9Zs0YLFixQVVWVxx3kfXx89P/+3//TtGnTmrRBAAAAAADuZI0K7lu2bNHcuXMVGRmplJQUhYaGyuVy6csvv9Tq1as1b948+fv767HHHmuufgEAAAAAuKNYnE7nTT94ffDgwfLx8dHOnTvVpo1n5v/++++VkJCgqqoqHThwoMkbvdOFrSowugUAQDM4Oj3K6BbuSBWpE41uAQDQDHxXbDa6hWZxV2OKi4uLNWbMmHqhXZLatGmjMWPG6MSJE03WHAAAAAAAd7pGBXcfHx+VlJQ0OF9SUqKf/exnt9wUAAAAAAD4QaOCe2xsrNasWXPNU+EPHjyo//7v/9awYcOarDkAAAAAAO50jbo5XVpamg4dOqSkpCT1799fvXr1kiR9+eWXKiwsVOfOnZWWltYsjQIAAAAAcCdq1Ip7ly5ddODAAU2fPl3ffvutcnJylJOTo2+//VbPPvusDhw4oHvvvfemt/fpp59qwoQJ6tOnj/z8/LRx40aP+ZSUFPn5+Xm84uLiPGouXbqk2bNnq3v37goKCtKECRN07ty5xhwWAAAAAACm1agV96qqKlVVVWnBggVasGBBvfkzZ87o7rvvvunr3KuqqtS3b19NnDhRzzzzzDVrhg4dqjVr1rjft2vXzmN+3rx52rVrl/74xz/K399fL730ksaPH6/9+/fLy8urEUcHAAAAAID5NCq4v/jii/r8888bfNzb448/rvvvv1/Lli27qe3Fx8crPj5ekjR9+vRr1nh7e8tms11zrry8XH/605+0cuVKPfjgg5KkNWvWKDw8XPv27eN6ewAAAABAi9eoU+Vzc3OVkJDQ4HxCQoL+8pe/3HJTP5aXl6eePXsqMjJSzz33nL755hv33JEjR/Tdd98pNjbWPdalSxeFhoYqPz+/SfsAAAAAAMAIjVpxLykpUadOnRqct9lsunDhwi03dUVcXJxGjhyprl276vTp01qwYIESExO1b98+eXt7y+FwyMvLSwEBAR6fs1qtcjgcDW63uLi4yXoEAOBW8JtkjIb/NQMAaMla6u+q3W6/7nyjgnvHjh11/PjxBuePHz+ue+65pzGbvK6xY8e6/x4WFqaIiAiFh4drz549SkxMbPBzLpdLFoulwfkbfSmmtLfA6A4AAM2gRf4mtQIVRjcAAGgWrfV3tVGnyg8fPlzr16+/5mnon332mdavX6/hw4c3WXNX69y5s4KCgnTy5ElJUmBgoGpra1VWVuZRV1paKqvV2mx9AAAAAABwuzRqxX3evHnau3evHn74YcXFxalv376yWCw6evSoPvroI9lsNr300kvN1avKysp0/vx5983qIiIi1LZtW+Xm5mrcuHGSpHPnzqmoqEjR0dHN1gcAAAAAALdLo4K7zWZTbm6u0tLS9MEHH+jDDz+UJPn6+mr8+PFKS0tr8A7w11JZWelePa+rq9PZs2dVWFgof39/+fv7a+HChUpMTJTNZtPp06c1f/58Wa1W9w3y7rnnHk2aNEmvvPKKrFar+3FwYWFhGjp0aGMODQAAAAAAU2pUcJd+OD09MzNTLpdLpaWlcrlcslqt172mvCFffPGFRo4c6X6fnp6u9PR0TZw4UcuXL9exY8e0ZcsWlZeXy2azafDgwXrnnXfk6+vr/swbb7whLy8vJScnq6amRkOGDNHq1at5hjsAAAAAoFWwOJ1Ol9FN4MbCVnFzOgBojY5OjzK6hTtSRepEo1sAADQD3xWbjW6hWTTq5nQAAAAAAOD2IrgDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJmZocP/00081YcIE9enTR35+ftq4caPHvMvlUnp6unr37q1OnTrpkUce0fHjxz1qnE6npk6dqpCQEIWEhGjq1KlyOp238zAAAAAAAGg2hgb3qqoq9e3bVwsXLlT79u3rzWdkZGjlypVatGiRPv74Y1mtVo0ePVoVFRXumilTpqiwsFDbtm1TVlaWCgsLNW3atNt5GAAAAAAANJs2Ru48Pj5e8fHxkqTp06d7zLlcLmVmZur5559XUlKSJCkzM1N2u11ZWVlKTk5WUVGRPvroI+3evVvR0dGSpBUrVmjEiBEqLi6W3W6/vQcEAAAAAEATM+017qdOnVJJSYliY2PdY+3bt1dMTIzy8/MlSQUFBerQoYM7tEvSwIED5ePj464BAAAAAKAlM3TF/XpKSkokSVar1WPcarXq/PnzkiSHw6GAgABZLBb3vMViUceOHeVwOBrcdnFxcTN0DABA4/GbZIxORjcAAGgWLfV39UZni5s2uF/x41Au/XAK/dVB/WpX11ytRZ5Cv7fA6A4AAM2gRf4mtQIVNy4BALRArfV31bSnyttsNkmqt3JeWlrqXoUPDAxUaWmpXC6Xe97lcqmsrKzeSj0AAAAAAC2RaYN7165dZbPZlJub6x6rqalRXl6e+5r2qKgoVVZWqqDg/1ajCwoKVFVV5XHdOwAAAAAALZWhp8pXVlbq5MmTkqS6ujqdPXtWhYWF8vf3V3BwsFJSUrRs2TLZ7Xb17NlTS5culY+Pjx599FFJUmhoqOLi4pSamqqMjAy5XC6lpqbqoYcearWnSAAAAAAA7iyGBvcvvvhCI0eOdL9PT09Xenq6Jk6cqMzMTM2cOVPV1dWaPXu2nE6nIiMjlZ2dLV9fX/dn1q5dqzlz5mjMmDGSpBEjRmjx4sW3/VgAAAAAAGgOFqfT6bpxGYwWtoqb0wFAa3R0epTRLdyRKlInGt0CAKAZ+K7YbHQLzcK017gDAAAAAACCOwAAAAAApkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATM3VwT09Pl5+fn8erV69e7nmXy6X09HT17t1bnTp10iOPPKLjx48b2DEAAAAAAE3L1MFdkux2u4qKityvQ4cOuecyMjK0cuVKLVq0SB9//LGsVqtGjx6tiooKAzsGAAAAAKDpmD64t2nTRjabzf3q2LGjpB9W2zMzM/X8888rKSlJffv2VWZmpiorK5WVlWVw1wAAAAAANA3TB/evvvpKffr0Uf/+/fXUU0/pq6++kiSdOnVKJSUlio2Ndde2b99eMTExys/PN6hbAAAAAACaVhujG7ieAQMGaNWqVbLb7SotLdWSJUsUHx+vw4cPq6SkRJJktVo9PmO1WnX+/Hkj2gUAAAAAoMmZOrgPHz7c4/2AAQMUERGhTZs26f7775ckWSwWjxqXy1Vv7GrFxcVN2ygAAD8Rv0nG6GR0AwCAZtFSf1ftdvt1500d3K/WoUMH9e7dWydPnlRCQoIkyeFwqEuXLu6a0tLSeqvwV7vRl2JKewuM7gAA0Axa5G9SK8BtbAGgdWqtv6umv8b9x2pqalRcXCybzaauXbvKZrMpNzfXYz4vL0/R0dEGdgkAAAAAQNMx9Yr7yy+/rN/85jfq0qWL+xr3b7/9VhMnTpTFYlFKSoqWLVsmu92unj17aunSpfLx8dGjjz5qdOsAAAAAADQJUwf3r7/+WlOmTFFZWZk6duyoAQMGaO/evQoJCZEkzZw5U9XV1Zo9e7acTqciIyOVnZ0tX19fgzsHAAAAAKBpWJxOp8voJnBjYau4xh0AWqOj06OMbuGOVJE60egWAADNwHfFZqNbaBYt6hp3AAAAAADuNAR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMRaTXBft26d+vfvL5vNpgceeECHDh0yuiUAAAAAAG5Zqwju2dnZmjt3rv7whz/ok08+UVRUlMaNG6czZ84Y3RoAAAAAALekVQT3lStX6re//a2eeOIJhYaGasmSJbLZbHr77beNbg0AAAAAgFvSxugGbtXly5d15MgRzZgxw2M8NjZW+fn5BnXV9I5OjzK6BQAAWg3fFZuNbgEAgJvW4lfcy8rKVFtbK6vV6jFutVrlcDgM6goAAAAAgKbR4oP7FRaLxeO9y+WqNwYAAAAAQEvT4oN7QECAvLy86q2ul5aW1luFBwAAAACgpWnxwb1du3aKiIhQbm6ux3hubq6io6MN6goAAAAAgKbR4m9OJ0nPPvuspk2bpsjISEVHR+vtt9/WhQsXlJycbHRrAAAAAADckha/4i5JY8aMUXp6upYsWaLBgwfr8OHD2rp1q0JCQoxuDUAjrVu3Tv3795fNZtNsrMbyAAAJi0lEQVQDDzygQ4cOGd0SAAAtzqeffqoJEyaoT58+8vPz08aNG41uCcAtaBXBXZKmTJmiv/3tb3I4HNq/f78GDRpkdEsAGik7O1tz587VH/7wB33yySeKiorSuHHjdObMGaNbAwCgRamqqlLfvn21cOFCtW/f3uh2ANwii9PpdBndBABI0rBhwxQWFqb/+q//co/96le/UlJSktLS0gzsDACAluvee+/V4sWL9fjjjxvdCoCfqNWsuANo2S5fvqwjR44oNjbWYzw2Nlb5+fkGdQUAAAAYj+AOwBTKyspUW1tb7zGOVqu13uMeAQAAgDsJwR2AqVgsFo/3Lper3hgAAABwJyG4AzCFgIAAeXl51VtdLy0trbcKDwAAANxJCO4ATKFdu3aKiIhQbm6ux3hubq6io6MN6goAAAAwXhujGwCAK5599llNmzZNkZGRio6O1ttvv60LFy4oOTnZ6NYAAGhRKisrdfLkSUlSXV2dzp49q8LCQvn7+ys4ONjg7gA0Fo+DA2Aq69atU0ZGhkpKStSnTx+98cYbGjRokNFtAQDQohw4cEAjR46sNz5x4kRlZmYa0BGAW0FwBwAAAADAxLjGHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAANcVHh6ulJQUQ3s4cOCA/Pz8dODAgUZ9Ljw8XGPHjm2mrgAAuD0I7gAAGGTjxo3y8/PTZ599dtOfuV4QPXXqlPz8/LRixYqmarFZhYeHy8/Pz/0KCgpSbGysNm/ebHRrAACYShujGwAAAHeusLAwPffcc5KkkpIS/c///I9SUlJ06dIlPfnkk+66QYMG6cKFC2rXrp1BnQIAYByCOwAAuG1cLpcuXbqku+++W5LUqVMnjR8/3j0/ceJE3XfffcrMzPQI7nfddZf7MwAA3Gk4VR4AAJNwOByaMWOGwsLCFBgYqN69e2v8+PE6evToT95mSkqKbDabHA6HkpOTFRwcrK5du2rmzJmqqanxqL18+bLS0tLUq1cvBQUFKSkpSV9++eU1t/uvf/1LL7/8ssLDwxUYGKh+/frp1Vdf1aVLlzzq/Pz8lJqaqj//+c+KiYlRYGCgtm/f3mC/VqtVdrtd//u//+sxfq1r3H/q9/Xee+/JarVqxowZqquru24tAABmwIo7AAAm8cQTT+jo0aOaOnWqQkJCVFZWpkOHDunEiRMKCwv7ydutq6vT6NGjFRYWptdee01//etftX79egUEBOiVV15x1z3//PPatGmTkpKSNHjwYH3++ecaPXp0vYBfXV2thIQEnTp1Sk8++aR+8Ytf6G9/+5veeustffnll9q0aZNHfV5ent577z09/fTTstls6tWrV4O9fv/99/r666/l7+9/w+P6Kd/Xu+++q+nTp+upp57S4sWLZbFYbrgfAACMRnAHAMAEysvLlZeXp9dff10zZsxwj6empt7ytr/77juNGDFCL7/8siTpqaeektPp1Pr1693B/ejRo9q0aZP+/d//XW+99Zb7s/Pnz9fy5cs9trdq1SoVFxdr3759Cg0NdY/36dNHs2bN0qFDhxQTE+MeLyoq0v79+9W/f/9r9lZWVibph2vcMzIyVFJSomeeeea6x/RTvq/169crNTVVM2bM0GuvvXbd7QMAYCacKg8AgAncfffdatu2rQ4ePKiLFy82+fYnT57s8X7QoEEqKytTRUWFJGnPnj2SVO+xb9OnT6+3rR07dig6OlodO3ZUWVmZ+zV06FBJ0ieffOJRHx0dfc3QfqW2R48e6tGjh2JiYrR161YlJyffMFg39vvKzMzUzJkzNXv2bEI7AKDFYcUdAAAT8Pb2VlpamtLS0mS32zVgwAANHz5cjz32mIKDgxu1ratP/27btq06d+7sMebn5ydJunjxonx9fXXmzBlZLBb17NnTo65jx47u2iv+8Y9/6O9//7t69Ohxzf2XlpZ6vO/WrVuDvd53331KS0tTbW2tjh07pqVLl8rhcKhNm+v/E6Ux31d+fr7+8pe/6JlnntG8efOuu10AAMyI4A4AgEn87ne/U0JCgnbt2qV9+/ZpyZIlWr58uTZt2qQHHnhA0g8rzdXV1df8/JVxb29vj/G77mr4BDuXy+Xx5/Vqrqirq9OQIUP0+9///pr1QUFBHu/bt2/f4Lb/7d/+zb1SP2zYMPXu3Vvjxo3T6tWr9eyzzzb4Oenmvi9J6tWrl6qrq7V9+3ZNnjxZdrv9utsFAMBsOFUeAAAT6datm6ZPn66tW7fqr3/9q7y9vbVixQr3fHBwsP7xj39c87NX7gAfEhLS6P2GhITI5XLpxIkTHuOlpaUqLy/3GPvFL36hyspKDR069Jqv69187kaGDx+uBx54QEuXLlVlZeUN62/0fUmSv7+/duzYIV9fXyUlJemrr776yf0BAGAEgjsAACbw7bff1ltJ79Kli6xWq5xOp3ts+PDhKikp0XvvvedR+/333+vtt9/Wz372Mw0aNKjR+4+Pj5f0w7XgP7Zq1ap6tWPGjNHnn3+uXbt21Zurrq6+qcB9PTNnztTFixf1zjvvNFhzs9/XFZ06ddJ7772nu+66S0lJSfr6669vqUcAAG4nTpUHAMAETpw4ocTERI0aNUq9e/eWt7e3PvzwQxUVFen111931z355JPasGGDnnrqKf32t79VRESE/vWvfyknJ0dffPGF0tPT612TfjP69eun8ePHa8OGDaqoqHA/Dm7fvn0KCAjwqJ0xY4Y+/PBDTZo0SY899pgiIyN16dIlnThxQjt27NC2bdt0//33/+TvIjY2VmFhYVq5cqWmTp1a79R/6ea/rx8LDg5WTk6OHn74YY0aNUoffPCBrFbrT+4TAIDbheAOAIAJdOnSRePGjdMnn3yirKwsWSwW9ejRQ2+++aYmTZrkrmvfvr127dqlJUuW6IMPPtCWLVvk7e2t8PBwrV+/XklJST+5hzfffFOBgYHavHmz9u7dq/vvv19//vOfNXbsWI+69u3bKycnRxkZGcrOztb27dvl4+Ojbt26KSUlpUmuIf/d736nlJQUbdq0ScnJyfXmb/b7ulr37t21Y8cOJSQkaPTo0dq5c+dP+o8OAABuJ4vT6Wz4bjQAAAAAAMBQXOMOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADCx/w9f4WgLV4VUfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 'IsUnderRisk'\n",
    "\n",
    "sns.countplot(train[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Unique : 43\n",
      "Test Unique :  34\n",
      "Test - Train : 2 -- [{34, 36}]\n"
     ]
    }
   ],
   "source": [
    "col = 'City'\n",
    "\n",
    "print(\"Train Unique : {}\\nTest Unique :  {}\\nTest - Train : {} -- [{}]\".format(\n",
    "    train[col].nunique(), test[col].nunique(),\n",
    "    len(set(test[col].unique()) - set(train[col].unique())), set(test[col].unique()) - set(train[col].unique())\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad156c5bd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AwEBJ0tSpU3XnnXfqyy+/1Lp16zRv3jwtXLhQd999d1mnCwAAAABwo9xcU75gwQLZ7XZNmjTJNczpdGrOnDlq1aqVgoKCFBMToz179liYZdny8/PThQsXXK/bt2+vH3/8USEhIWrWrFm+fwEBAa7pwsPDdf/99+v999/X3XffrbfeessVT1K+mAAAAAAA65SLpnzbtm1KTExUVFRUvuEvvPCC4uPjNW/ePK1bt06BgYEaMGCAsrOzLcq0bIWGhurbb7/VwYMHdeLECY0aNUpZWVkaMWKE/v3vf+vAgQP6+uuvNX78eGVnZ+v06dOaOHGiUlJSdPDgQf373/9WamqqWrZsKUkKCQmRzWbTmjVrdPz4ceXk5Fg8hwAAAABQtVnelGdmZmr06NF66aWXZLfbXcOdTqcSEhI0YcIE9e/fX5GRkUpISFBOTo5WrFhhYcZl56GHHpKfn5+6du2q8PBwnT17VmvWrFG1atU0aNAgde3aVRMnTpSfn59q1aql6tWrKyMjQ7Gxsbr66qv1t7/9TVdffbWeeeYZSVLjxo01depUzZ49WxEREfnOSgAAAAAAlD1bRkaG08oERowYodDQUM2cOVMxMTGKjIzU/PnzdeDAAXXo0EHr1q3TVVdd5Zr+zjvv1OWXX65XXnnF48/IzMxU/fr1SyP9KodlWb5ELdrqdppdYzuXQSYAAAAoD9ztH7JvWP5YeqO3xMRE7d+/X4sXLy40Lj09XdL/v4HZRYGBgTpy5EiZ5AcAAAAAQGmyrClPS0vTrFmz9Pnnn7tuQFYUm82W77XT6Sw0rGDcgmrXrq1atWqZTxYuWVlZOnbsmNVpwICiagIAAABVE/uGZS8iIqLE8ZY15Vu3btWJEyd0zTXXuIZduHBBmzZt0uuvv67U1FRJ0rFjxxQcHOya5vjx44WOnl+qqBnOzMxU7dq1fZh91VWvXj2FhIRYnQYu+tL96evufgQAAABQibjZP2TfsPyxrCmPiYlRx44d8w174IEHFB4erkceeUTNmzeXw+FQcnKy65ryM2fOaPPmzZo1a5YVKQMAAAAA4FOWNeV2uz3f3dYlqW7dumrQoIEiIyMlSbGxsVqwYIEiIiLUvHlzxcXFyd/fX4MHD7YiZQAAAAAAfMrSG725M378eJ0+fVqTJk1SRkaGoqOjlZSUpICAAMOx3F2LDvecTktv1A8AAAAAlY7lj0QrC+fPn1d2drbsdjuNuRdyc3NVo0YNbppXjvBINAAAAFyKR6JVPOX6SLmv1KhRQwEBAcrKyrI6lQqNhhwAAAAAfKtKNOXSnw1l/fr1rU4DAAAAAACXKtOUo3LjNB0AAAAAFVE1qxMAAAAAAKCqoikHAAAAAMAiNOUAAAAAAFiEphwAAAAAAIvQlAMAAAAAYBGacgAAAAAALEJTDgAAAACARXhOOQAAAACgSFGLtrqdZtfYzmWQSeXFkXIAAAAAACxCUw4AAAAAgEVoygEAAAAAsAjXlAPIh+uGAAAAgLLDkXIAAAAAACxCUw4AAAAAgEUMNeXt27fXZ599Vuz41atXq3379l4nBQAAAABAVWCoKf/111+Vm5tb7Pjc3FwdOnTI66QAAAAAAKgKDJ++brPZih23b98+BQQEeJUQAAAAAABVhdu7r7/zzjtavny563VcXJwSExMLTZeRkaHdu3frlltu8W2GAAAAAABUUm6b8tzcXKWnp7teZ2ZmKi8vL980NptNdevW1T333KMpU6b4PksAAAAAACoht0356NGjNXr0aElSu3btNHfuXPXp06fUEwMAAAAAoLJz25RfaseOHaWVBwAAAAAAVY6hpvyi7OxsHT58WCdPnpTT6Sw0vnv37l4nBgAAAABAZWeoKT958qQmT56sf/3rX7pw4UKh8U6nUzabTf/73/98liAAAAAAAJWVoab84Ycf1qpVqzR69Gh1795ddru9tPICAAAAAKDSM9SUf/XVVxozZoyeeeaZ0soHAAAAAIAqo5qRif38/BQeHl5auQAAAAAAUKUYasr79++vL7/8srRyAQAAAACgSjF0+vpDDz2kkSNH6v7779fIkSMVEhKi6tWrF5ouMDDQbawlS5bon//8pw4dOiRJatWqlSZOnKhbbrlF0p83jZs7d64SExOVkZGh6OhoxcXFqXXr1kZSLreiFm11O82usZ3LIBMAAAAAgFUMNeXR0dGy2Wz6/vvv9f777xc7nSd3X2/cuLFmzpyp8PBw5eXlafny5Ro+fLi+/vprtWnTRi+88ILi4+MVHx+viIgIPfvssxowYIC2bdumgIAAI2kDAAAAAFAuGWrKH3vsMdlsNp98cExMTL7X06ZN02uvvaZt27YpKipKCQkJmjBhgvr37y9JSkhIUEREhFasWKERI0b4JAcAAAAAAKxkqCmfOnVqqSRx4cIFffTRR8rNzVXnzp118OBBpaenq2fPnq5p6tSpo27dumnLli005QAAAACASsFQU+5ru3btUu/evXXmzBn5+/tr2bJlioqK0pYtWyQVvjY9MDBQR44cKTFmWlpaqeVb1irTvFitKi/L0pj3qrw8AQAAKjL2DcteREREieMNNeXz5s1zO43NZtNjjz3mUbyIiAilpKQoMzNTK1euVGxsrFatWpUv1qWcTqfb0+fdzXC58aX7G71VmHkpD9wsz0q7LEtjPWLdBAAAqLh8vV/MvmGpM9SUz507t9hxNpvN1TR72pT7+fmpWbNmkqSOHTvqu+++06JFizRx4kRJ0rFjxxQcHOya/vjx4x7d2R0AAAAAgIrA0HPKT548WejfiRMntH37do0ZM0YdO3bUvn37TCeTl5ens2fPKiwsTA6HQ8nJya5xZ86c0ebNm9WlSxfT8QEAAAAAKE8MNeVFBqhWTU2bNtWcOXMUFhamKVOmePS+GTNmaNOmTTp48KB27dqlmTNnasOGDRoyZIhsNptiY2P1/PPPa+XKldq9e7fGjh0rf39/DR482NuUAQAAAAAoF3x6o7frrrtOM2fO9Gja9PR03XfffTp27Jjq1aunqKgorVixQjfddJMkafz48Tp9+rQmTZqkjIwMRUdHKykpiWeUAwAAAAAqDZ825WlpaXI6nR5Nm5CQUOJ4m82mqVOnltpj2AAAAAAAsJqhpnzjxo1FDs/MzFRKSoqWLFmiO+64wyeJAQAAAABQ2Rlqym+//fYiH0nmdDpVvXp1DRo0yKPHpgEAAAAAAINN+SeffFJomM1mk91uV2hoKNd7AwAAAABggKGm/Nprry2tPAAAAAAAqHJM3egtOztbGzZs0K+//ipJCg0N1bXXXsuRcgAAAAAADDDclC9evFizZ89Wbm5uvjut+/v7a9q0aRozZoxPE0TlE7Voa4njd43tXEaZAAAAoDJifxMViaGm/N1339WUKVMUHR2t2NhYtWzZUk6nUz/99JNeeeUVTZ06VQ0aNNCdd95ZWvkCAAAAAFBpGGrK4+Pj1aVLF61atUo1avz/t7Zt21b9+/fX7bffrpdeeommHAAAAAAAD1QzMnFaWpoGDhyYryG/qEaNGho4cKD27dvns+QAAAAAAKjMDB0p9/f3V3p6erHj09PTVbduXa+TAgAAAIDyhOvUUVoMHSnv2bOnFi9erJSUlELjNmzYoFdffVU33XSTz5IDAAAAAKAyM3SkfPr06dq0aZP69++vdu3aqUWLFpKkn376STt27FCjRo00ffr0UkkUAAAAAIDKxtCR8uDgYKWkpGjs2LE6deqUVq5cqZUrV+rUqVN64IEHlJKSoiZNmpRWrgAAAAAAVCqGjpTn5uYqNzdXs2fP1uzZswuNP3TokGrXrs115QAAACg3uBYYQHlm6Ej5448/rr/+9a/Fjh8+fLimTZvmdVIAAAAAAFQFhpry5ORk3X777cWOv/3227V27VqvkwIAAAAAoCow1JSnp6crKCio2PEOh0NHjx71OikAAAAAAKoCQ015w4YNtWfPnmLH79mzR/Xr1/c6KQAAAAAAqgJDTXmvXr2UmJioLVu2FBq3bds2JSYmqlevXj5LDgAAAACAyszQ3denTp2qL7/8Un369NHNN9+syMhI2Ww27dq1S1999ZUcDoeeeOKJ0soVAAAAAIBKxVBT7nA4lJycrOnTp+vTTz/VF198IUkKCAjQ0KFDNX36dDkcjlJJFAAAAACAysZQUy5JV155pRISEuR0OnX8+HE5nU4FBgbKZrOVRn4wgGdwoijZDw8rcXzAc8vLKBMAAAAABRluyi+y2WwKDAz0ZS4AAAAAAFQphm70BgAAAAAAfIemHAAAAAAAi9CUAwAAAABgEZpyAAAAAAAsQlMOAAAAAIBFaMoBAAAAALCI6UeieWvhwoX65JNPtG/fPvn5+alTp06aPn26IiMjXdM4nU7NnTtXiYmJysjIUHR0tOLi4tS6dWur0gYAAACACiv74WEljg94bnkZZYKLLDtSvmHDBo0cOVJr1qzRypUrVaNGDd1xxx06efKka5oXXnhB8fHxmjdvntatW6fAwEANGDBA2dnZVqUNAAAAAIDPWHakPCkpKd/rxYsXKzQ0VKmpqbrtttvkdDqVkJCgCRMmqH///pKkhIQERUREaMWKFRoxYoQVaQMAAAAA4DPl5prynJwc5eXlyW63S5IOHjyo9PR09ezZ0zVNnTp11K1bN23ZssWqNAEAAAAA8Jly05RPmTJFbdu2VefOnSVJ6enpkqTAwMB80wUGBurYsWNlnh8AAAAAAL5m2enrl3r88ceVmpqq1atXq3r16vnG2Wy2fK+dTmehYZdKS0srlRyt4Ot5qSjLpjTyrCjzbgUzy4blCQCoTNiuVT3sbxaPfUPfi4iIKHG85U351KlTlZSUpE8++URNmzZ1DXc4HJKkY8eOKTg42DX8+PHjhY6eX8rdDJcbX251O4nheXETs9wsm9LIs6LMu695sB65U2jZlMa6CQCAlarqfkJVVpX3N73cP2TfsOxZevr65MmTtWLFCq1cuVItWrTINy4sLEwOh0PJycmuYWfOnNHmzZvVpUuXsk4VAAAAAACfs+xI+cSJE/Xee+9p2bJlstvtrmvI/f39ddlll8lmsyk2NlYLFixQRESEmjdvrri4OPn7+2vw4MFWpQ0AAAAAgM9Y1pQvXbpUklyPO7to8uTJmjp1qiRp/PjxOn36tCZNmqSMjAxFR0crKSlJAQEBZZ4vAAAoWdSikk9x3DW2cxllAgBAxWFZU56RkeF2GpvNpqlTp7qadAAAAAAAKpNy80g0AAAAAACqGppyAAAAAAAsYvkj0QCgvOB6WAAAAJQ1jpQDAAAAAGARmnIAAAAAACxCUw4AAAAAgEW4phwAAABAlZL98DC30wQ8t7wMMqkc3C1PlmXJOFIOAAAAAIBFaMoBAAAAALAITTkAAAAAABbhmvJyjGszAAAAAKBy40g5AAAAAAAWoSkHAAAAAMAiNOUAAAAAAFiEa8pRoqhFW0scv2ts5zLKBAAAAAAqH46UAwAAAABgEZpyAAAAAAAsQlMOAAAAAIBFuKbcA+6uq5a4trq845nvAIrCfTMAAIDVOFIOAAAAAIBFaMoBAAAAALAITTkAAAAAABahKQcAAAAAwCI05QAAAAAAWISmHAAAAAAAi9CUAwAAAABgEZ5T7iMV4TnY7nKUykeeAAAAZaki7McBqLw4Ug4AAAAAgEVoygEAAAAAsAhNOQAAAAAAFrG0Kd+4caPuuusutW7dWna7XW+//Xa+8U6nU3PmzFGrVq0UFBSkmJgY7dmzx6JsAQAAAADwLUub8tzcXEVGRmru3LmqU6dOofEvvPCC4uPjNW/ePK1bt06BgYEaMGCAsrOzLcgWAAAAAADfsrQp7927t5566in1799f1arlT8XpdCohIUETJkxQ//79FRkZqYSEBOXk5GjFihUWZQwAAAAAgO+U22vKDx48qPT0dPXs2dM1rE6dOurWrZu2bNliYWYAAAAAAPhGuX1OeXp6uiQpMDAw3/DAwEAdOXKk2PelpaWVal5mlUZe5SFmecihonzmHV+edDvNR70alHoeBZmZ9/JaZ6Wtqs53VcZ37lvungV9dOysMsoEMIbfgqqpPOwXW6Gy7N+XJxERESWOL7dN+UU2my3fa6fTWWjYpdzNsCllciSnAAAeD0lEQVRfbvU6RKG8KklMU8vbgpjulMp6U5AHORrOw6L1qEyWlxVKY91E+cZ37lsV4bcYVRPrZtVTUfaLS4Ov1/fSWJbIp9yevu5wOCRJx44dyzf8+PHjhY6eAwAAAABQEZXbpjwsLEwOh0PJycmuYWfOnNHmzZvVpUsXCzMDAAAAAMA3LD19PScnR/v375ck5eXl6fDhw9qxY4caNGigkJAQxcbGasGCBYqIiFDz5s0VFxcnf39/DR482Mq0AQAAAADwCUub8u3bt6tv376u13PmzNGcOXM0bNgwJSQkaPz48Tp9+rQmTZqkjIwMRUdHKykpSQEBARZmDQAAAACAb1jalF933XXKyMgodrzNZtPUqVM1derUMswKAAAAAICyUW6vKQcAAAAAoLIr949EMypqkftb9u8a27kMMoFZ7p5jK0kBzy0vg0wAAAAAoHRxpBwAAAAAAIvQlAMAAAAAYBGacgAAAAAALFLprikHAFivstzfozLf48Ldd1QRvh9UTqybqKgq8zYDpYsj5QAAAAAAWISmHAAAAAAAi9CUAwAAAABgEa4pB8oRd9cilZfrkCpKnr5WWa8VqyjXf1eUPIHyimu14Qv8FgO+x5FyAAAAAAAsQlMOAAAAAIBFaMoBAAAAALAI15TDK5X1GltUfKybgHeq6r0jAAAoaxwpBwAAAADAIjTlAAAAAABYhKYcAAAAAACLcE05AAAAuBcHAFiEI+UAAAAAAFiEphwAAAAAAIvQlAMAAAAAYJEqeU05z14FylbUoq0ljt81trPPY6YajgiUX6VRQyjf3H3nUvn43qvyulmV5x2Ab3GkHAAAAAAAi9CUAwAAAABgEZpyAAAAAAAsUiWvKQdQvvBsXPhKebhnCNeZAqWLbQYqE7f3yEl7rsTxrOuVA0fKAQAAAACwCE05AAAAAAAWoSkHAAAAAMAiFeKa8qVLl+rFF19Uenq6WrVqpTlz5qhbt25Wp4VKjutC4QtWPKO9oqyb5eH6byuUxvWwlfkaW1+v76Xx/G9PYpbGdaEVoYYq87rpa1Y9m7401iNfx2Q9qnys+i32dUxf1WS5P1KelJSkKVOm6NFHH9U333yjzp07a8iQITp06JDVqQEAAAAA4JVy35THx8frr3/9q+655x61bNlS8+fPl8Ph0Ouvv251agAAAAAAeMWWkZHhtDqJ4pw9e1aNGjXSa6+9pjvuuMM1fOLEidq9e7c+++wzC7MDAAAAAMA75fpI+YkTJ3ThwgUFBgbmGx4YGKhjx45ZlBUAAAAAAL5Rrpvyi2w2W77XTqez0DAAAAAAACqact2UX3HFFapevXqho+LHjx8vdPQcAAAAAICKplw35X5+furQoYOSk5PzDU9OTlaXLl0sygoAAAAAAN8o988pf+CBBzRmzBhFR0erS5cuev3113X06FGNGDHC6tQAAAAAAPBKuT5SLkkDBw7UnDlzNH/+fF133XVKTU3V+++/r9DQUMOxli5dqnbt2snhcKhHjx7atGmTV7lt3LhRd911l1q3bi273a63337bq3gLFy7UjTfeqJCQEIWHh2vo0KHavXu3VzGXLFmibt26KSQkRCEhIerVq5fWrFnjVcxLLViwQHa7XZMmTfIqzpw5c2S32/P9a9Gihdf5HT16VPfff7/Cw8PlcDjUpUsXbdiwwXS8tm3bFsrTbrfrzjvvNBXvwoULmj17tmu9bNeunWbPnq3z58+bzlGSsrOzNWXKFLVp00ZBQUHq3bu3vvvuO4/f727ddjqdmjNnjlq1aqWgoCDFxMRoz549XsVcuXKlBg4cqPDwcNntdqWkpHiV57lz5zR9+nR169ZNjRs3VsuWLTVq1CgdOnTIqzxnz56tq6++Wo0bN1ZYWJj69eunLVu2eBXzUuPHj5fdbtdLL73kVczY2NhC6+nNN9/sdZ779u3T3/72N4WGhqpRo0a6/vrrtXfvXlPxiqolu92uiRMnms4xJydHkyZNUmRkpIKCgtSpUyfFx8d7Nd/Hjh1TbGysWrVqpUaNGmnQoEH6+eefS4zpye+5kTryJJ7RGnIX00wNeZKn0Royum30pIY8iWm0hjzN00gNeRLTaB15EtNIHXkSz2gNudt3MbMNchfTzDaopJhmt0Hu8jSzDTKyL+jpNshdTDPbIE/yNFI/7uKZ2Qa5i2lmG+QuppltUEFF7a+bqSN3Mc3UUXHxzNaQuxzN1JC7mJfytIYKKvdNuSSNGjVKO3fu1LFjx7R+/Xp1797dcIykpCRNmTJFjz76qL755ht17txZQ4YMMfTFFpSbm6vIyEjNnTtXderUMR3nog0bNmjkyJFas2aNVq5cqRo1auiOO+7QyZMnTcds3LixZs6cqfXr1ys5OVnXX3+9hg8frv/85z9e57tt2zYlJiYqKirK61iSFBERob1797r+eftHk4yMDN1yyy1yOp16//33tWXLFj377LNe3Y8gOTk5X47r16+XzWbL98g+I55//nktXbpU8+bN09atWzV37lwtWbJECxcuNJ2jJI0bN07r1q1TQkKCNm3apBtvvFF33HGHfvvtN4/e727dfuGFFxQfH6958+Zp3bp1CgwM1IABA5SdnW065qlTp9S5c2c988wzHs9nSTFPnTqlH374QRMnTtT69ev1zjvv6L///a8GDx5c4h893OUZERGhuLg4bdq0SatXr1ZYWJgGDx5c4hMhPP2t+Pjjj/Xdd9+pUaNGXs37RTfccEO+9fWDDz7wKuaBAwd0yy23KCwsTCtXrtTmzZv15JNPyt/f31S8S3Pbu3ev3n33XUkqsZ7cxXziiSf0xRdf6JVXXtGWLVv06KOPaubMma7YRmM6nU4NHz5c+/fv19tvv61vvvlGISEh6t+/v3Jzc4uN6cnvuZE68iSe0RpyF9NMDXmSp9EaMrJt9LSGPI1ppIY8iWm0hjyJabSOPIlppI7cxTNTQ+72Xcxsg9zFNLMNKimm2W2QuzzNbIM83Rc0sg3yJKbRbZC7mEbrx108M9sgdzHNbINKiml2G3Sp4vbXzdSRu5hm6qi4eGZryF2OZmrIXcyLjNRQQeX6OeW+dNNNNykqKkovvviia9hVV12l/v37a/r06V7Hb9KkiZ599lkNHz7c61gX5eTkKDQ0VG+//bZuu+02n8Vt2rSppk+f7tUlAJmZmerRo4deeOEFPfvss4qMjNT8+fNNx5szZ47rB9ZXZs2apY0bN/r0zICC4uLi9OKLL+rHH39U3bp1Db9/6NChatCggV555RXXsPvvv18nT57Ue++9Zyqn06dPKzg4WG+++aZiYmJcw3v06KFevXrpySefNBSv4LrtdDrVqlUrjR492vXX5NOnTysiIkJPP/20R+tVSfVy4sQJhYeH65NPPtF1111nOs+i/Pjjj+ratas2btzo0R+TPImZlZWl0NBQffjhh7rppptMx/z11191yy236KOPPtLgwYN133336aGHHnIbr7iYsbGx+t///md6PSoq5qhRo2Sz2bRkyRKfxCto3Lhx2rRpk/7973+bjnnNNdeob9++evzxx13D+vTpo6ioKI9+owrG3Ldvnzp16qSUlBS1bdtWkpSXl6cWLVroqaee0v/93/95lGvB33Nv66ik7YPZGvJkm2O0hjyJabSGiovpTQ0VFdPbGioqpjc1VFzMgozWUVExvamjgvF8VUMX913+/ve/e70NKhjz0veYrZ+SYl5ktH48iWm0foqL6U39FBXT2/opKqa39VMwXkFGa6eomN5ugwrGvO6667yqn+L2173ZBnnSAxipIyM9hac1ZCSmpzXkLqa3NVQhjpR76+zZs/r+++/Vs2fPfMN79uxp6HSFspaTk6O8vDzZ7XafxLtw4YI+/PBD5ebmqnPnzl7FmjBhgvr3768ePXr4JDfpz7+Atm7dWu3atdO9996rAwcOeBXv008/VXR0tEaMGKHmzZvr2muv1auvviqn0zd/h3I6nXrrrbc0dOhQUw25JHXt2lUbNmzQTz/9JOnPH5uUlBT16tXLdF7nz5/XhQsXVLt27XzD69Sp45M/ehw8eFDp6en56qlOnTrq1q1bua4nSa6//vqqps6ePavExETVq1fPtbE04/z58xo1apQmTpyoli1b+iQ3Sdq8ebOaN2+u6OhojRs3Tr///rvpWHl5eVq9erVatmypQYMGKTw8XDfeeKOSkpJ8kmtOTo6SkpJ0zz33eBWna9euWr16tQ4fPixJ2rJli/7zn/8Y2lm91B9//CFJ+eqpWrVqqlWrlqF6Kvh77m0d+Xr74GlMozXkLqaZGioqprc1VFye3tRQwZi+qCF3y9NMHRUV05s6KhjP2xoquO/ii22QL/eHjMQ0Wj/uYpqpn6Jiels/xeXpTf0UjOlt/bhblmZqp6iY3m6DCsb0tn6K21/3po583QMYiedpDXka00gNlRTTF/tx5f5Gb75w4sQJXbhwodBpy4GBgR6dqmCVKVOmqG3btl5vMHbt2qXevXvrzJkz8vf317Jly7w65TwxMVH79+/X4sWLvcrrUp06ddKiRYsUERGh48ePa/78+erdu7dSU1N1+eWXm4p54MABvfbaaxo7dqwmTJignTt3avLkyZKk++67z+uck5OTdfDgQd19992mY0yYMEE5OTnq0qWLqlevrvPnz2vixIkaNWqU6ZgBAQHq3Lmz4uLi1Lp1azkcDq1YsUJbt25Vs2bNTMe9KD09XZKKrKcjR454Hb+0nD17Vk8++aRuvfVWNWnSxKtYq1ev1siRI3Xq1CkFBQXpX//6l6688krT8ebMmaMGDRpo5MiRXuV1qZtvvll9+/ZVWFiYfv31V82ePVv9+vXT119/rVq1ahmO9/vvvysnJ0cLFy7U448/runTp+ubb77R6NGjVbduXd16661e5btixQr98ccfGjZsmFdx5s2bp4cfflht2rRRjRp/buKeffZZ0/m1aNFCISEhmjVrll588UX5+/tr0aJF+u9//+uqBU8U/D33to58tX0wEtNMDRUX05saKiqmtzVUVExva6hgTF/UkLvvyEwdFRXTmzoqGM9sDRW373KxYTBTO77eHzIS00j9uItppn5Kimm2fkqKabZ+iouZnp5uqn48/X6M1E5JMc3WTnExz507Z3obVNL+utltkK97ACPxPK0hT2IarSF3MX2xH1clmvKLbDZbvtdOp7PQsPLi8ccfV2pqqlavXq3q1at7FSsiIkIpKSnKzMzUypUrFRsbq1WrVikyMtJwrLS0NM2aNUuff/65/Pz8vMrrUgWPDHfq1EkdOnTQO++8owcffNBUzLy8PHXs2NF1eUL79u21f/9+LV261CdNeWJioq666iq1a9fOdIykpCS9++67Wrp0qVq1aqWdO3dqypQpCg0N9fiUvqIsXrxYDzzwgCIjI1W9enW1b99egwcP1g8//GA6ZkEVqZ7Onz+v++67T5mZmVq+fLnX8a677jqlpKToxIkTSkxM1N///nd9+eWXCgoKMhxrw4YNeueddwzdDMUTgwYNcv1/VFSUOnTooLZt22rNmjXq16+f4Xh5eXmS/jwN72JNtmvXTt9//72WLl3qdVOemJiomJgYNWzY0Ks4ixcv1pYtW7R8+XKFhIRo06ZNmjZtmkJDQ93eZKgoNWvW1FtvvaUHH3xQf/nLX1S9enXdcMMNhs5mKen33Ewd+XL74GlMMzVUUkyzNVRUTG9rqLg8vamhomJ6W0OefO9G66i4mGbrqKh4ZmuouH2Xi8zUji/3h4zENFo/7mKaqZ/iYv7vf/8zXT8l5Wm2foqL2aBBA0nG68fT79xI7ZQU02ztlBTTTP14ur9upI583QMYiedpDXka00gNuYvpq/24KtGUX3HFFapevXqho+LHjx/36qZfpWXq1KlKSkrSJ598oqZNm3odz8/Pz3WEtGPHjvruu++0aNEivfzyy4Zjbd26VSdOnNA111zjGnbhwgVt2rRJr7/+un777TdTR+EKuuyyy9SqVSvt37/fdAyHw1HoFJIWLVq4Tinyxu+//67PPvtMcXFxXsV56qmn9OCDD7o2XlFRUTp06JCee+45r5ryv/zlL/rss8+Um5ur7OxsBQUFacSIEQoLC/MqX+nP5Sr9eTfQ4OBg1/DyWk/nz5/XyJEjtXv3bq1atcr0mReX8vf3V7NmzdSsWTNdffXVuuqqq/Tmm2/qscceMxwrJSVFR48ezbeuXrhwQdOnT1dCQoLXT2C4qFGjRmrcuLHpmrriiitUo0aNImvK21PYd+zYoe3bt+upp57yKs7p06c1a9YsvfHGG65rY9u0aaOdO3fqpZdeMtWUS1KHDh20YcMGZWZm6ty5c2rYsKFuuukmdezY0e17i/s9N1tHvt4+eBLTTA25i2mmhoqL6U0NGVmentZQcTG9qSFP8jRaR8XFNFtHJeVopoaK23e5eP2rmW2QL/eHPI1ppn7cxTRTP8XFbNKkien6MbI8Pa2f4mIuXLjQVP14kqPR2iku5vz5801vg0rK00z9uNtfT01NlWSsjnzdA3gaz0gNeRrTSA25izlu3Dif7MdViabcz89PHTp0UHJycr47KiYnJ5s6YlSaJk+erKSkJK1atconjwQrSl5ens6ePWvqvTExMYV+BB544AGFh4frkUce8dnR8zNnzigtLc3UTVYu6tq1q/bt25dv2L59+xQSEuJtenrnnXdUq1YtDRw40Ks4p06dKnTEo3r16q4jKt7y9/eXv7+/MjIytHbtWs2aNcvrmGFhYXI4HEpOTtZVV10l6c/va/PmzT6J70vnzp3Tvffeqz179mjVqlWuRsjXvKmpUaNGqX///vmGDRo0SIMGDfL6+upLnThxQkeOHDG9DPz8/HTVVVcpLS0t33Bf1FRiYqJCQ0N1ww03eBXn3LlzOnfuXKnVVP369SVJP//8s7Zv364nnniixOlL+j03U0elsX1wF9NMDZnJ010NlRTTbA0ZzdOTGiopptka8jRPI3VUUkwzdeRpjkZr6FIX1xFfboO8+e32JKavtkHu8jQzHxff48ttUEl5mN0GXYzpq21QUTl6uw26GNOX26Ci8jRSP+7215s3b264jnzdA3gSz2gNmc2xpHXXXcyGDRtqyJAh+cabqaEq0ZRLfy68MWPGKDo6Wl26dNHrr7+uo0ePenUH8pycHNdf/PLy8nT48GHt2LFDDRo0MLWTOnHiRL333ntatmyZ7Ha763oPf39/XXbZZaZynDFjhnr37q0mTZooJydHK1as0IYNG/T++++binfxGY6Xqlu3rho0aODV6V8XrxEJDg52XVN+6tQpr64vHTt2rHr37q24uDgNHDhQO3bs0Kuvvqpp06aZjin9eWrPm2++qYEDByogIMCrWLfeequef/55hYWFqVWrVtqxY4fi4+N11113eRV37dq1ysvLU0REhH755RdNmzZNERERHj8dwN26HRsbqwULFigiIkLNmzdXXFyc/P39NXjwYNMxT548qUOHDikzM1OS9Msvv6h+/fpyOBzF/giXFLNRo0a65557tH37di1fvlw2m81VU/Xq1Sv2MWIlxaxfv75efPFF3XrrrXI4HDpx4oSWLFmi3377rcRHqLib94J/la5Ro4YcDociIiJMxWzQoIHmzp2rfv36yeFw6Ndff9WsWbMUGBio22+/3XSe48aN04gRI9StWzddf/31SklJUVJSUrHPXffkN/LUqVP64IMPNG7cOI8uf3AXs3v37po5c6b8/f0VEhKijRs36t1339XMmTNNx/zoo490+eWXKzQ0VLt27dKUKVMUExNT6Oahl3L3e26z2QzVkSfbB6M15C7m+fPnDdeQu5hZWVmGa8hdzMDAQMM15C5mTk6O4Rry5DsyWkOe7hcYqSN3MevVq2eojjzJ0WgNlbTvYrR2PIkpGa8fdzHN1I+7mGbqx11MM/XjLqaZ+vHkOzJaP57sAxvdBpUU02jteJqnmW2QJ/vrRuvIk5hG6shdPDM15C6mmRryZL4LXo/uSQ0VkpGR4awq/+Li4pwhISFOPz8/Z/v27Z2ffvqpV/E++eQTp6RC/4YNG2YqXlGxJDknT55sOsdhw4Y5g4ODnX5+fs6GDRs6e/To4fzwww99uly7d+/uHD16tFcxBg4c6AwKCnLWrFnT2ahRI2ffvn2dqampXuf23nvvOaOiopy1atVyhoeHO+fOnes8efKkVzFXrlzplORcu3at1/kdOnTIef/99zuDg4OdtWvXdoaFhTkfeeQR59GjR72K+89//tPZtGlTp5+fn9PhcDhHjx7tPHjwoMfvd7dunzx50jl58mSnw+Fw1qpVy9mtWzfnpk2bvIoZHx9veP0vKeYPP/xQbE3Fx8ebivnbb785Y2JinEFBQU4/Pz9nUFCQ87bbbnN+9dVXXs17wX8hISHOp59+2nTMI0eOOHv27Ols2LChs2bNms7g4GDnsGHDnP/5z3+8zjM+Pt4ZHh7urF27tjMyMtK5dOlSr+K9/PLLzurVqzv37Nnjk3Vz7969zr/+9a/ORo0aOWvXru2MiIhwPv300yXWvbuYc+fOdTZp0sS1LCdOnOg8duxYiXl68ntupI48iWe0htzFNFND7mKaqSEz20Z3NeQuppka8jRPIzXkaUwjdeRJTCN15Ek8ozXkbt/FzDbIXUwz26CSYprdBpUU0+w2yOi+oCfboJJimt0GeZKnkfrxJJ7RbZC7mGa2Qe5imtkGFfWv4P66mTpyF9NMHRUXz2wNlRTTbA25m28zNVTwX5V5TjkAAAAAAOVNlXhOOQAAAAAA5RFNOQAAAAAAFqEpBwAAAADAIjTlAAAAAABYhKYcAAAAAACL0JQDAAAAAGARmnIAACBJio2NVdu2ba1OAwCAKoWmHACASu7333/XjBkz1LVrVzVu3FiNGjVSt27dNGPGDB09erTE986fP1+rVq0qo0wBAKh6bBkZGU6rkwAAAKVj+/btGjJkiLKzszVo0CBFR0erWrVq2rVrlz788ENdfvnl+vbbbyVJ586dU15enmrVquV6v8Ph0MCBA5WQkGDVLAAAUKnVsDoBAABQOjIyMjR8+HDZbDZ9/fXXat26db7x06ZN0/PPP+96XbNmzbJOEQCAKo/T1wEAqKTeeOMN/fbbb5o9e3ahhlyS6tevr+nTp7teF7ym3G63648//tDy5ctlt9tlt9sVExOjn3/+WXa7XfHx8YVi/vjjj7Lb7Xr11VdLZ6YAAKhkaMoBAKikPv/8c9WuXVsDBgww9f7FixerZs2auuaaa7R48WItXrxYEydOVHh4uLp06aL33nuv0Hvee+891axZU4MGDfI2fQAAqgSacgAAKqm9e/eqefPm8vPzM/X+oUOHqlq1amratKmGDh2qoUOH6sYbb5QkDRs2TDt27NCPP/7omt7pdOqDDz7QTTfdpCuuuMIn8wAAQGVHUw4AQCWVnZ2tgICAUok9YMAA1a5dO9/R8g0bNujw4cO66667SuUzAQCojGjKAQCopAICApSdnV0qsevXr68+ffrogw8+kNP554Nc3n//fdWrV0+33nprqXwmAACVEU05AACVVMuWLbVv3z6dPXu2VOIPGzZMhw8f1saNG/XHH3/o448/1h133KHatWuXyucBAFAZ0ZQDAFBJ3XbbbTpz5ow++ugj0zFsNlux43r27KmgoCC99957+vzzz5WVlaWhQ4ea/iwAAKoimnIAACqpv//972rcuLGefPJJ7d27t9D4rKwszZo1q8QYdevWVUZGRpHjqlevriFDhujjjz/WW2+9pdDQUHXr1s0nuQMAUFXQlAMAUEnZ7Xa9/fbbysvLU48ePfTggw/q9ddf1xtvvKFJkyapQ4cOWrlyZYkxOnbsqPXr1+ull17Shx9+qPXr1+cbP2zYMGVlZWnt2rW68847SzyyDgAACrNlZGQ4rU4CAACUnt9//10vv/yyVq9erV9//VVOp1PNmjXTbbfdpjFjxujKK6+UJMXGxmrDhg3auXOn671paWl65JFH9N133yk3N1fdu3fXp59+mi9+jx499MMPP2jbtm2KiIgo03kDAKCioykHAABe6dWrl/Ly8rR27VqrUwEAoMLh9HUAAGDa7t27tW3bNg0bNszqVAAAqJA4Ug4AAAzbvXu3vv/+e73yyiv673//qx9++EGXXXaZ1WkBAFDhcKQcAAAY9vHHH+uBBx7QqVOn9Nprr9GQAwBgEkfKAQAAAACwCEfKAQAAAACwCE05AAAAAAAWoSkHAAAAAMAiNOUAAAAAAFiEphwAAAAAAIvQlAMAAAAAYJH/B9BQ/tU9V0piAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data[col], hue=data['source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad1554d5d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train[col], hue=train['IsUnderRisk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Location_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 'Location_Score'\n",
    "\n",
    "plt.figure()\n",
    "sns.distplot(train[col], color='b', label='train', hist=False)\n",
    "sns.distplot(test[col], color='g', label='test', hist=False)\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(train[col][train[target] == 1], color='b', label='1') # , hist=False\n",
    "sns.distplot(train[col][train[target] == 0], color='g', label='0') # , hist=False\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internal_Audit_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Unique : 13\n",
      "Test Unique :  13\n",
      "Test - Train : 0 -- [set()]\n"
     ]
    }
   ],
   "source": [
    "col = \"Internal_Audit_Score\"\n",
    "\n",
    "print(\"Train Unique : {}\\nTest Unique :  {}\\nTest - Train : {} -- [{}]\".format(\n",
    "    train[col].nunique(), test[col].nunique(),\n",
    "    len(set(test[col].unique()) - set(train[col].unique())), set(test[col].unique()) - set(train[col].unique())\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad15274390>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data[col], hue=data['source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad152fc890>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train[col], hue=train['IsUnderRisk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule 1\n",
    "\n",
    "### We can see that for Internal_Score >=9 target is always == 1.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### External_Audit_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Unique : 13\n",
      "Test Unique :  13\n",
      "Test - Train : 0 -- [set()]\n"
     ]
    }
   ],
   "source": [
    "col = \"External_Audit_Score\"\n",
    "\n",
    "print(\"Train Unique : {}\\nTest Unique :  {}\\nTest - Train : {} -- [{}]\".format(\n",
    "    train[col].nunique(), test[col].nunique(),\n",
    "    len(set(test[col].unique()) - set(train[col].unique())), set(test[col].unique()) - set(train[col].unique())\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad1529f610>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data[col], hue=data['source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad1512d410>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train[col], hue=train['IsUnderRisk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule 2\n",
    "\n",
    "### We can see that for External_Score >=9 target is always == 1.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fin_Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Unique : 13\n",
      "Test Unique :  13\n",
      "Test - Train : 0 -- [set()]\n"
     ]
    }
   ],
   "source": [
    "col = \"Fin_Score\"\n",
    "\n",
    "print(\"Train Unique : {}\\nTest Unique :  {}\\nTest - Train : {} -- [{}]\".format(\n",
    "    train[col].nunique(), test[col].nunique(),\n",
    "    len(set(test[col].unique()) - set(train[col].unique())), set(test[col].unique()) - set(train[col].unique())\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad150e7250>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data[col], hue=data['source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad15019ad0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train[col], hue=train['IsUnderRisk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule 3\n",
    "\n",
    "### We can see that for Fin_Score >=9 target is always == 1.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Unique : 10\n",
      "Test Unique :  7\n",
      "Test - Train : 0 -- [set()]\n"
     ]
    }
   ],
   "source": [
    "col = \"Loss_score\"\n",
    "\n",
    "print(\"Train Unique : {}\\nTest Unique :  {}\\nTest - Train : {} -- [{}]\".format(\n",
    "    train[col].nunique(), test[col].nunique(),\n",
    "    len(set(test[col].unique()) - set(train[col].unique())), set(test[col].unique()) - set(train[col].unique())\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad14f4b3d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data[col], hue=data['source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad1525a790>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ak4qLixUREaHnnntOPj4+puNUC2NHymNiYrRgwQLZ7XYVFBRozpw56t27t3bs2KG8vDxJktVqdXuP1WrVoUOHLrrd3NzcGstcUwJMB7iA2jiX8H58381g3lGf8H0HUJs1adJEjRs3Nh3jsiktLfVo/VtvvVW33nqrJMlisej06dMeb6OmHDt2TPn5+eXG7Xb7Rd9nrJT36tXL7XVMTIw6duyo5cuXq3PnzpJ+meRfczqd5cZ+q6IP7I2KTAe4gNo4l/B+fN/NYN5Rn/B9B1CbHT16VE2aNDG2/9OXeX+X8lktFouuvPJKo/P1a1dffbWCgoI8fp/XPBLtqquuUnh4uPbt2+e6zvy3f2UoKCgod/QcAAAAAIDaymtKeWlpqXJzc2Wz2RQSEiKbzaasrCy35du3b1dsbKzBlAAAAAAAVB9jp69PmzZNd9xxhwIDA13XlJ84cULDhw+XxWJRQkKC5s6dK7vdrrCwMKWkpMjX11eDBw82FRkAAI/wCEAAAFARY6X8p59+0iOPPKLCwkJde+21iomJ0aZNmxQcHCxJmjhxokpKSjRlyhQ5HA5FR0dr9erVPKMcAAAAAFBnGCvlS5Ysuehyi8WipKQkJSUlXaZEAAAAAABvdvz4ce3bt0+SVFZWpoMHD2rXrl1q3rx5lW6y5g2MlXLANE4rBQAAAGqXzz77THfffbfrdXJyspKTkzV8+HClpaUZTFZ1lHIAAAAAgCTvP0DUrVs3ORwO0zGqldfcfR0AAAAAgPqGUg4AAAAAgCGUcgAAAAAADKGUAwAAAABgCKUcAAAAAABDKOUAAAAA4EWcTqfpCPDQpfzOKOUAAAAA4CV8fX3lcDgo5rWI0+mUw+GQr69vld7Pc8oBAAAAwEs0bNhQfn5+OnbsmOko8ICfn58aNqxavaaUAwAAAIAXadiwoZo1a2Y6Bi4TTl8HAAAAAMAQSjkAAAAAAIZQygEAAAAAMIRSDgAAAACAIZRyAAAAAAAMoZQDAAAAAGAIpRwAAAAAAEMo5QAAAAAAGEIpBwAAAADAEEo5AAAAAACGNDQdAPVD5IKPTEcoZ4fpAAAAAADqPY6UAwAAAABgiNeU8rlz58rf319TpkxxjTmdTiUnJys8PFwBAQHq27ev9uzZYzAlAAAAAADVxytKeU5OjtLT0xUZGek2Pm/ePKWmpur555/X+++/L6vVqgEDBqioqMhQUgAAAAAAqo/xUn706FGNHj1aL7/8svz9/V3jTqdTaWlpSkxMVHx8vCIiIpSWlqbjx48rIyPDYGIAAAAAAKqHR6X8xhtv1Pr16y+4/J133tGNN97oUYBzpbt79+5u4wcOHFBeXp569uzpGvPx8VFcXJx27tzp0T4AAAAAAPBGHt19/fvvv1dxcfEFlxcXF+uHH36o9PbS09O1b98+LVy4sNyyvLw8SZLVanUbt1qtOnToUKX3AQAAAACAt/L4kWgWi+WCy/bu3Ss/P79KbSc3N1czZ87U22+/rUaNGlV6f06n86IZcnNzK7V/bxJgOsAF1Ma5rAvq+rzzfTeDeTeDeTeDeQcAeBO73X7R5RWW8uXLl2vFihWu1ykpKUpPTy+3nsPh0FdffaU+ffpUKthHH32kwsJC/f73v3eNnT17Vtu2bdOSJUu0Y8cvT5HOz89XYGCga52CgoJyR89/raIP7I289bZ11TqXm7zvOeXeqjZ+hz1RL77vXoh5N4N5N4N5BwDUJhWW8uLiYtep5NIvN2YrKytzW8disahp06Z68MEHNXXq1ErtuG/fvurUqZPb2Pjx4xUaGqo//vGPCgsLk81mU1ZWlm666SZJUmlpqbZv366ZM2dWah8AAAAAAHizCkv56NGjNXr0aElShw4d9Nxzz+muu+665B37+/u73W1dkpo2barmzZsrIiJCkpSQkKC5c+fKbrcrLCxMKSkp8vX11eDBgy95/wAAAAAAmObRNeW7du2qqRznNXHiRJWUlGjKlClyOByKjo7W6tWrK33dOgAAAAAA3szjG71JUlFRkQ4ePKgjR47I6XSWW37zzTdXKcy6devcXlssFiUlJSkpKalK2wMAAAAAwJt5VMqPHDmiJ598UmvWrNHZs2fLLT93Z/Sff/652gICAAAAAFBXeVTKJ02apLVr12r06NG6+eaby10TDgAAAAAAKs+jUv7uu+/q0Ucf1V//+teaygMAAAAAQL1xhScrN2rUSKGhoTWVBQAAAACAesWjUh4fH69NmzbVVBYAAAAAAOoVj0r5hAkTdPjwYY0dO1Y5OTk6fPiw/vvf/5b7AQAAAAAAFfPomvLo6GhZLBZ9/vnnWrly5QXX4+7rAAAAAABUzKNS/sQTT8hisdRUFgAAAAAA6hWPSnlSUlJN5QAAAAAAoN7x6JpyAAAAAABQfTw6Uv78889XuI7FYtETTzxR5UAAAAAAANQXHpXy55577oLLLBaLnE4npRwAAAAAgEryqJQfOXKk3FhZWZm+//57LVy4UDt37lRGRka1hQMAAAAAoC675GvKr7jiCrVu3VrJyckKCQnR1KlTqyMXAAAAAAB1nkdHyivSrVs3zZgxozo3CQC1TuSCj0xHKGeH6QCos/i+AwBwaar17uu5ublyOp3VuUkAAAAAAOosj46Ub9269bzjR48eVXZ2tl577TXdc8891RIMAAAAAIC6zqNS3q9fP1kslnLjTqdTDRo00KBBgyr12DQAAAAAAOBhKf/3v/9dbsxiscjf31/BwcHy8/OrtmA1hWvfAAAAAADewqNSfsstt9RUDgAAAAAA6p0q3X29qKhIW7Zs0ffffy9JCg4O1i233FIrjpQDAAAAAOAtPC7lCxcu1KxZs1RcXOx2p3VfX1899dRTevTRR6s1IAAAAAAAdZVHpfyf//ynpk6dqujoaCUkJKhdu3ZyOp365ptv9OqrryopKUnNmzfXkCFDaiovAAAAAAB1hkfPKU9NTVVsbKzeeecdDRo0SDfccIOioqI0aNAgvf322+rSpYtefvnlSm3rtddeU1xcnIKCghQUFKRevXppw4YNruVOp1PJyckKDw9XQECA+vbtqz179nj26QAAAAAA8GIeHSnPzc3VzJkz1bBh+bc1bNhQAwcO1PTp0yu1rVatWmnGjBkKDQ1VWVmZVqxYoREjRuiDDz7QDTfcoHnz5ik1NVWpqamy2+2aPXu2BgwYoJycHK5dByqJpw0AAAAA3s2jI+W+vr7Ky8u74PK8vDw1bdq0Utvq27evevXqpTZt2igsLExPPfWUrrrqKuXk5MjpdCotLU2JiYmKj49XRESE0tLSdPz4cWVkZHgSGQAAAAAAr+VRKe/Zs6cWLlyo7Ozscsu2bNmiRYsW6bbbbvM4xNmzZ7Vq1SoVFxerS5cuOnDggPLy8tSzZ0/XOj4+PoqLi9POnTs93j4AAAAAAN7Io9PXp0+frm3btik+Pl4dOnRQ27ZtJUnffPONdu3apd/97neVPn1dknbv3q3evXurtLRUvr6+ev311xUZGekq3lar1W19q9WqQ4cOeRIZAAAAAACv5VEpDwwMVHZ2tl544QVt3LhRmZmZkn55Tvn48eM1adIkXXPNNZXent1uV3Z2to4eParMzEwlJCRo7dq1ruUWi8VtfafTWW7st3Jzcz34RLgY5tIM5t0M5t2Muj7vAaYDXEBdn3dvxbwDQP1kt9svutyjUl5cXKzi4mLNmjVLs2bNKrf8hx9+UJMmTSp9XXmjRo3Upk0bSVKnTp306aefasGCBZo8ebIkKT8/X4GBga71CwoKyh09/62KPrA2ed+Nr7xVhXPpCea90ph3M5h3M6p13r1QkekAF8D33Yy6/n0HAFSNR9eU//nPf9Z99913weUjRozQU089VeUwZWVlOnXqlEJCQmSz2ZSVleVaVlpaqu3btys2NrbK2wcAAAAAwJt4dKQ8KytLI0aMuODyfv36afny5ZXa1jPPPKPevXvruuuuc91VfcuWLVq5cqUsFosSEhI0d+5c2e12hYWFKSUlRb6+vho8eLAnkQEA9QSPAAQAALWRR6U8Ly9PAQEXvkLOZrPp8OHDld7WmDFjlJ+fr6uvvlqRkZHKyMhw3b194sSJKikp0ZQpU+RwOBQdHa3Vq1fzjHIAAAAAQJ3hUSm/9tprtWfPngsu37Nnj5o1a1apbaWlpV10ucViUVJSkpKSkjyJCAAAAABAreHRNeW9evVSenr6eZ8VnpOTo/T0dPXq1avawgEAAAAAUJd5dKQ8KSlJmzZt0l133aXbb79dERERslgs2r17t959913ZbDb95S9/qamsAAAAAADUKR6V8nN3RJ8+fbrWrVunjRs3SpL8/Pw0dOhQTZ8+XTabrUaCAgAAAABQ13hUyiWpZcuWSktLk9PpVEFBgZxOp6xWqywWS03kAwAAAACgzvK4lJ9jsVhktVqrMwsAAAAAAPWKRzd6AwAAAAAA1YdSDgAAAACAIZRyAAAAAAAMoZQDAAAAAGAIpRwAAAAAAEMo5QAAAAAAGEIpBwAAAADAEEo5AAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADKGUAwAAAABgCKUcAAAAAABDKOUAAAAAABhCKQcAAAAAwBBKOQAAAAAAhlDKAQAAAAAwhFIOAAAAAIAhlHIAAAAAAAwxVspfeOEF/c///I+CgoIUGhqqoUOH6quvvnJbx+l0Kjk5WeHh4QoICFDfvn21Z88eQ4kBAAAAAKhexkr5li1b9PDDD2vDhg3KzMxUw4YNdc899+jIkSOudebNm6fU1FQ9//zzev/992W1WjVgwAAVFRWZig0AAAAAQLVpaGrHq1evdnu9cOFCBQcHa8eOHbrzzjvldDqVlpamxMRExcfHS5LS0tJkt9uVkZGhkSNHmogNAAAAAEC18Zpryo8fP66ysjL5+/tLkg4cOKC8vDz17NnTtY6Pj4/i4uK0c+dOUzEBAAAAAKg2xo6U/9bUqVMVFRWlLl26SJLy8vIkSVar1W09q9WqQ4cOXXA7ubm5NReynmEuzWDezWDezWDezWDezWDeAaB+stvtF13uFaX8z3/+s3bs2KF33nlHDRo0cFtmsVjcXjudznJjv1bRB9amj6qcs76pcC49wbxXGvNuBvNuBvNuBvNuRrXOOwCgzjB++npSUpJWrVqlzMxMtW7d2jVus9kkSfn5+W7rFxQUlDt6DgAAAABAbWS0lD/55JPKyMhQZmam2rZt67YsJCRENptNWVlZrrHS0lJt375dsbGxlzsqAAAAAADVztjp65MnT9Ybb7yh119/Xf7+/q5ryH19fXXVVVfJYrEoISFBc+fOld1uV1hYmFJSUuTr66vBgwebig0AAAAAQLUxVsoXL14sSa7HnZ3z5JNPKikpSZI0ceJElZSUaMqUKXI4HIqOjtbq1avl5+d32fMCAAAAAFDdjJVyh8NR4ToWi0VJSUmukg4AAAAAQF1i/EZvAAAAAADUV5RyAAAAAAAMoZQDAAAAAGAIpRwAAAAAAEMo5QAAAAAAGEIpBwAAAADAEEo5AAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADKGUAwAAAABgCKUcAAAAAABDKOUAAAAAABhCKQcAAAAAwBBKOQAAAAAAhlDKAQAAAAAwhFIOAAAAAIAhlHIAAAAAAAyhlAMAAAAAYAilHAAAAAAAQyjlAAAAAAAYQikHAAAAAMAQSjkAAAAAAIYYLeVbt27VsGHD1L59e/n7+2vZsmVuy51Op5KTkxUeHq6AgAD17dtXe/bsMZQWAAAAAIDqZbSUFxcXKyIiQs8995x8fHzKLZ83b55SU1P1/PPP6/3335fVatWAAQNUVFRkIC0AAAAAANXLaCnv3bu3nn76acXHx+uKK9yjOJ1OpaWlKTExUfHx8YqIiFBaWpqOHz+ujIwMQ4kBAAAAAKg+XntN+YEDB5SXl6eePXu6xnx8fBQXF6edO3caTAYAAAAAQPVoaDrAheTl5UmSrFar27jVatWhQ4cu+L7c3NwazVWfMJdmMO9mMO9mMO9mMO9mMO8AUD/Z7faLLvfaUn6OxWJxe+10OsuN/VpFH1ibPqqOWPVChXPpCea90ph3M5h3M5h3M5h3M6p13gEAdYbXnr70pM6HAAAUAUlEQVRus9kkSfn5+W7jBQUF5Y6eAwAAAABQG3ltKQ8JCZHNZlNWVpZrrLS0VNu3b1dsbKzBZAAAAAAAVA+jp68fP35c+/btkySVlZXp4MGD2rVrl5o3b66goCAlJCRo7ty5stvtCgsLU0pKinx9fTV48GCTsQEAAAAAqBZGS/lnn32mu+++2/U6OTlZycnJGj58uNLS0jRx4kSVlJRoypQpcjgcio6O1urVq+Xn52cwNQAAAAAA1cNoKe/WrZscDscFl1ssFiUlJSkpKekypgIAAAAA4PLw2mvKAQAAAACo6yjlAAAAAAAYQikHAAAAAMAQSjkAAAAAAIZQygEAAAAAMIRSDgAAAACAIZRyAAAAAAAMoZQDAAAAAGAIpRwAAAAAAEMo5QAAAAAAGEIpBwAAAADAEEo5AAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADKGUAwAAAABgCKUcAAAAAABDGpoOAAAAgNqvaNJw0xHOy+/FFaYjoA7i+47qxJFyAAAAAAAMoZQDAAAAAGAIp68DAAAAtRSnUQO1H0fKAQAAAAAwhFIOAAAAAIAhtaKUL168WB06dJDNZlP37t21bds205EAAAAAALhkXn9N+erVqzV16lTNnTtXXbt21eLFi3Xvvfdqx44dCgoKMh0PAAAAAHAZ1NV7KHj9kfLU1FTdd999evDBB9WuXTvNmTNHNptNS5YsMR0NAAAAAIBLYnE4HE7TIS7k1KlT+t3vfqe//e1vuueee1zjkydP1ldffaX169cbTAcAAAAAwKXx6iPlhYWFOnv2rKxWq9u41WpVfn6+oVQAAAAAAFQPry7l51gsFrfXTqez3BgAAAAAALWNV5fyFi1aqEGDBuWOihcUFJQ7eg4AAAAAQG3j1aW8UaNG6tixo7KystzGs7KyFBsbaygVAAAAAADVw+sfiTZ+/Hg9+uijio6OVmxsrJYsWaLDhw9r5MiRpqMBAAAAAHBJvPpIuSQNHDhQycnJmjNnjrp166YdO3Zo5cqVCg4ONh3tkrz22muKi4tTUFCQgoKC1KtXL23YsMF0rHpl7ty58vf315QpU0xHqfOSk5Pl7+/v9tO2bVvTseqFw4cPa+zYsQoNDZXNZlNsbKy2bNliOladFhUVVe777u/vryFDhpiOVqedPXtWs2bNUocOHWSz2dShQwfNmjVLZ86cMR2tzisqKtLUqVN1ww03KCAgQL1799ann35qOladsnXrVg0bNkzt27eXv7+/li1b5rbc6XQqOTlZ4eHhCggIUN++fbVnzx5DaeuOiuY9MzNTAwcOVGhoqPz9/ZWdnW0oad1S0bzPmjVLnTt3VqtWrRQSEqL+/ftr586dhtJWD68/Ui5JjzzyiB555BHTMapVq1atNGPGDIWGhqqsrEwrVqzQiBEj9MEHH+iGG24wHa/Oy8nJUXp6uiIjI01HqTfsdrvWrl3ret2gQQODaeoHh8OhPn36qGvXrlq5cqVatGihAwcOcE+OGpaVlaWzZ8+6Xh8+fFg9evRwe7Qnqt9LL72kxYsXKy0tTREREdq9e7cSEhLUqFEjPfHEE6bj1WmPP/64du/erbS0NF133XV64403dM8992jHjh1q1aqV6Xh1QnFxsSIiIjR8+HCNHTu23PJ58+YpNTVVqampstvtmj17tgYMGKCcnBz5+fkZSFw3VDTvJ06cUJcuXTRkyJDzLkfVVDTvdrtdKSkpCgkJUUlJiRYsWKDBgwfrk08+UcuWLQ0kvnRe/Zzy+qZ169aaPn06p+bXsKNHj6p79+6aN2+eZs+erYiICM2ZM8d0rDotOTlZmZmZ2r59u+ko9crMmTO1detWzsIxLCUlRfPnz9fXX3+tpk2bmo5TZw0dOlTNmzfXq6++6hobO3asjhw5ojfeeMNgsrqtpKREgYGBWrp0qfr27esa7969u3r16qVp06YZTFc3XXfddZo9e7ZGjBgh6Zej5OHh4Ro9erQmT54s6Zffi91u17PPPsu/K6vJb+f91woLCxUaGqp///vf6tatm4F0ddfF5v2cY8eOKTg4WKtWrdJtt912GdNVH68/fb0+OHv2rFatWqXi4mJ16dLFdJw6LzExUfHx8erevbvpKPXK/v371b59e3Xo0EGjRo3S/v37TUeq89atW6fo6GiNHDlSYWFhuuWWW7Ro0SI5nfwt9nJxOp363//9Xw0dOpRCXsO6du2qLVu26JtvvpEkff3118rOzlavXr0MJ6vbzpw5o7Nnz6pJkyZu4z4+Pvwh9jI5cOCA8vLy1LNnT9eYj4+P4uLiav0pvUBFTp06pfT0dF199dWKiooyHafKasXp63XV7t271bt3b5WWlsrX11evv/46p1PXsPT0dO3bt08LFy40HaVeiYmJ0YIFC2S321VQUKA5c+aod+/e2rFjh6655hrT8eqs/fv3629/+5vGjRunxMREffnll3ryySclSWPGjDGcrn7IysrSgQMHdP/995uOUuclJibq+PHjio2NVYMGDXTmzBlNnjy5zl3+5m38/PzUpUsXpaSkqH379rLZbMrIyNBHH32kNm3amI5XL+Tl5UlSuUuTrFarDh06ZCISUOPeeecdPfzwwzpx4oQCAgK0Zs2aWnvqukQpN8putys7O1tHjx5VZmamEhIStHbtWkVERJiOVifl5uZq5syZevvtt9WoUSPTceqV3x6piomJUceOHbV8+XI99thjhlLVfWVlZerUqZOmT58uSbrxxhu1b98+LV68mFJ+maSnp+umm25Shw4dTEep81avXq1//vOfWrx4scLDw/Xll19q6tSpCg4O1gMPPGA6Xp22cOFCjR8/XhEREWrQoIFuvPFGDR48WF988YXpaPWKxWJxe+10OsuNAXVFt27dlJ2drcLCQqWnp+uhhx7Spk2bFBAQYDpalXD6ukGNGjVSmzZtXP9ojoqK0oIFC0zHqrM++ugjFRYW6ve//71atGihFi1aaOvWrVq8eLFatGihkydPmo5Yb1x11VUKDw/Xvn37TEep02w2m9q1a+c21rZtWx08eNBQovrlv//9r9avX68HH3zQdJR64emnn9Zjjz2mQYMGKTIyUsOGDdP48eP14osvmo5W511//fVav369fvzxR+3evVvvv/++Tp8+rZCQENPR6gWbzSZJys/PdxsvKCjgxp6os3x9fdWmTRt17txZr7zyiq688kotXbrUdKwqo5R7kbKyMp06dcp0jDqrb9++2rZtm7Kzs10/nTp10qBBg5Sdnc3R88uotLRUubm5rn9IoGZ07dpVe/fudRvbu3evgoKCDCWqX5YvX67GjRtr4MCBpqPUCydOnCj3VIcGDRqorKzMUKL6x9fXVwEBAXI4HHrvvfd01113mY5UL4SEhMhmsykrK8s1Vlpaqu3btys2NtZgMuDyqe09itPXDXnmmWfUu3dvXXfddTp+/LgyMjK0ZcsWrVy50nS0Ouvcs4J/rWnTpmrevDmXDNSwadOm6Y477lBgYKDrmvITJ05o+PDhpqPVaePGjVPv3r2VkpKigQMHateuXVq0aJGeeuop09HqPKfTqaVLl2rgwIE8jugyueOOO/TSSy8pJCRE4eHh2rVrl1JTUzVs2DDT0eq89957T2VlZbLb7fruu+/01FNPyW63X/RuyfDM8ePHXWeXlZWV6eDBg9q1a5eaN2+uoKAgJSQkaO7cubLb7QoLC1NKSop8fX01ePBgw8lrt4rm/ciRI/rhhx909OhRSdJ3332nZs2ayWazceDhElxs3ps1a6b58+frjjvukM1mU2FhoV577TX99NNPtfrRozwSzZCEhARlZ2crPz9fV199tSIjI/X444/X2tv411Z9+/blkWiXwahRo7Rt2zYVFhbq2muvVUxMjP7yl78oPDzcdLQ6b8OGDZo5c6b27t2rwMBAjR49Wo8++ijXGdawzZs3q3///nrvvfcUHR1tOk69UFRUpL/+9a9au3atCgoKZLPZNGjQID3xxBPl7gyO6rVmzRrNmDFDP/30k5o3b67+/ftr2rRpatasmelodUZ2drbuvvvucuPDhw9XWlqanE6nnnvuOf3jH/+Qw+FQdHS0UlJSOOhwiSqa92XLlmn8+PHllj/55JNKSkq6HBHrpIvN+9y5czV69Gh98skn+vnnn3XNNdeoU6dO+tOf/qSYmBgDaasHpRwAAAAAAEO4phwAAAAAAEMo5QAAAAAAGEIpBwAAAADAEEo5AAAAAACGUMoBAAAAADCEUg4AAAAAgCGUcgAAAAAADKGUAwBwGSxbtkz+/v7KyckxHQUAAHgRSjkAAAAAAIZQygEAgNcqKSkxHQEAgBpFKQcAwEscOXJEf/zjH9WuXTu1bNlSXbp00SuvvCKn0+m23ocffqg777xTISEhuu666xQTE6M//elPbussXrxYcXFxatWqlVq3bq3u3btryZIlHuWpzH5OnTqlOXPmqHPnzmrZsqXsdruGDx+uPXv2uNYpKSnRM888o6ioKLVs2VIdOnTQrFmzdPLkSbdtRUVFadCgQdq8ebNuv/122Ww2vfTSS67lWVlZ6tevnwIDA9WqVSv169dPO3fu9OgzAQDgbRqaDgAAAKSTJ0/q7rvv1p49ezRq1Ci1bdtWGzdu1LRp0/Tjjz8qOTlZkvT1119ryJAhioiI0NSpU9W0aVPt379fGzZscG1r6dKlmjx5svr376/Ro0fr9OnT+vrrr7Vjxw6NGjWqUnkqs5+ysjINHz5c7733nmtfJSUlys7O1ueff6727dvL6XTq/vvv17vvvqthw4YpJiZGO3bsUEpKivbs2aNly5a57Xffvn164IEH9MADD+gPf/iDAgMDJUkZGRkaM2aMunXrpr/85S8qKyvTsmXL1L9/f61bt04xMTGX+isAAMAIi8PhcFa8GgAAuBTLli3T+PHjtWnTJnXu3Lnc8kWLFumJJ57Q/Pnz9cADD0iSq9CuW7dOH3/8sUJDQ5WWlqakpCR9++23atGixXn3NWLECO3bt0/bt2+vct7K7OfcZ5o2bZomT57stszpdMpiseidd97RsGHDNHnyZE2bNs21PCkpSWlpaXrzzTfVo0cPSb8cKf/hhx+0fPly3XXXXa51i4uLFRkZqTvvvFNpaWmu8RMnTqhr165q3bq1MjMzq/xZAQAwidPXAQDwAhs2bFCLFi00YsQI15jFYtHjjz8up9OpjRs3SpL8/PwkSevWrVNZWdl5t+Xn56cff/xRn3zySZXzVGY/mZmZatasmSZMmFBumcVicX0ui8Wixx57zG35xIkTXct/7brrrnMr5NIvp607HA4NGTJEhYWFrp+SkhL16NFD27dv1+nTp6v2QQEAMIxSDgCAF/j+++8VGhqqBg0auI23a9fOtVySBg0apNjYWD3++OMKCwvTQw89pJUrV7qV0sTERF111VW67bbb1LFjR02aNEkffvihR3kqs5/vvvtOYWFhaty48UU/l81mk7+/v9t4QECAmjVr5vpc54SEhJTbxrfffitJGjBggEJDQ91+li5dqtOnT+vo0aMefT4AALwF15QDAFCL+Pj46O2339aWLVv07rvv6r333tOYMWP0yiuvaMOGDfLx8VF4eLhycnJcyzds2KC///3vGjlypF588cVq28+5U9Sr6rc3sDu33986d6R+wYIFatWq1Xm3dfXVV1c5BwAAJnGkHAAALxAcHKx9+/bp7NmzbuPffPONa/k5V1xxhW699VbNnDlTW7du1dy5c7Vr1y79+9//dq3j6+ur+Ph4zZ8/X7t27dK9996rv//97/rpp58qnami/bRp00Z79+7VqVOnLvq58vLy5HA43Mbz8vJ07Ngxt891Iddff70k6dprr1WPHj3O+9OoUaNKfy4AALwJpRwAAC/Qp08fFRQUaMWKFa4xp9Opl19+WRaLRb1795Yk/fzzz+Xee+ONN0qSq/j+dp2GDRsqMjLSbZ2KVGY//fv3l8PhUGpqarl1zx0F79Onj5xOpxYsWOC2fP78+a7lFbntttvUrFkzpaSklHuMmiQVFBRUuA0AALwVp68DAHAZLV++XB988EG58aFDh2rp0qVKTEzUl19+qbCwMG3atEkbN27U2LFjFRoaKkmaPXu2tmzZoj59+ig4OFgOh0NLliyRr6+v7rjjDkm/XHtttVrVtWtXtWzZUt99950WLVqkiIgIhYeHVypnZfYzbNgwrVy5UjNmzNAXX3yhm2++WaWlpdqyZYsGDBigYcOGqU+fPrr99ts1e/ZsHTx4UDfddJM++ugjrVy5UnfddZfrzusX4+fnp3nz5unhhx/WLbfconvvvVc2m00//vijsrOz5evrq4yMjMr9AgAA8DI8Eg0AgMvg3OPDLmTz5s0KDAzUs88+q3Xr1unIkSMKCQnRgw8+qMcee8x17fbmzZu1aNEiffrppyooKNA111yjzp0764knnlBUVJQk6R//+If+9a9/6euvv1ZRUZECAgJ0xx13aMqUKbJarZXKW5n9SFJpaanmzp2rjIwMHTx4UM2bN1dMTIyefvpp1x8ASkpK9Nxzz2nVqlXKy8tTQECAhg4dqilTprjdJC4qKkpt27bVqlWrzptp586deuGFF7Rz506VlJTIZrMpJiZGDzzwQKXKPQAA3ohSDgAAAACAIVxTDgAAAACAIVxTDgBAPXL27NkKb4zWpEkTNWvW7DIlAgCgfqOUAwBQjxw8eNB1F/ULGT58uNLS0i5TIgAA6jdKOQAA9YjNZtObb7550XUCAgIuUxoAAMCN3gAAAAAAMIQbvQEAAAAAYAilHAAAAAAAQyjlAAAAAAAYQikHAAAAAMAQSjkAAAAAAIb8P/v+8c+J/lbSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train[col], hue=train['IsUnderRisk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule 4\n",
    "\n",
    "### We can see that for Loss_Score >=9 target is always == 1.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Past_Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Unique : 7\n",
      "Test Unique :  5\n",
      "Test - Train : 1 -- [{5}]\n"
     ]
    }
   ],
   "source": [
    "col = \"Past_Results\"\n",
    "\n",
    "print(\"Train Unique : {}\\nTest Unique :  {}\\nTest - Train : {} -- [{}]\".format(\n",
    "    train[col].nunique(), test[col].nunique(),\n",
    "    len(set(test[col].unique()) - set(train[col].unique())), set(test[col].unique()) - set(train[col].unique())\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad14db1350>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data[col], hue=data['source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad14d95390>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train[col], hue=train['IsUnderRisk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule 5\n",
    "\n",
    "### We can see that for Past_Results >=2 target is always == 1.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Rules : \n",
    "\n",
    "1. Internal_Audit_Score >= 9 ==> Target = 1\n",
    "1. External_Audit_Score >= 9 ==> Target = 1\n",
    "1. Final_Score >= 9 ==> Target = 1\n",
    "1. Loss_score >= 9 ==> Target = 1\n",
    "1. Past_Results >= 2 ==> Target = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the coverage i.e how much percentage of our rules cover the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Coverage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Coverage : 48.07%\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Coverage : {:.2f}%\".format((train[\n",
    "    (train['Internal_Audit_Score'] >= 9) | \n",
    "    (train['External_Audit_Score'] >= 9) | \n",
    "    (train['Fin_Score'] >= 9) | \n",
    "    (train['Loss_score'] >= 9) | \n",
    "    (train['Past_Results'] >= 2)\n",
    "].shape[0] / train.shape[0]) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Coverage : 47.64%\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Coverage : {:.2f}%\".format((test[\n",
    "    (test['Internal_Audit_Score'] >= 9) | \n",
    "    (test['External_Audit_Score'] >= 9) | \n",
    "    (test['Fin_Score'] >= 9) | \n",
    "    (test['Loss_score'] >= 9) | \n",
    "    (test['Past_Results'] >= 2)\n",
    "].shape[0] / test.shape[0]) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the sub-sample data\n",
    "\n",
    "This sub-sampled data id devoid of the records which could be predicted by the rules i.e devoid of the 48% of records for train and 47% for test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Train Shape : (282, 9)\n",
      "New Test Shape :  (122, 8)\n"
     ]
    }
   ],
   "source": [
    "def get_rules_ids(df):\n",
    "    idx_df = df[\n",
    "        (df['Internal_Audit_Score'] > 8) | \n",
    "        (df['External_Audit_Score'] > 8) | \n",
    "        (df['Fin_Score'] > 8) | \n",
    "        (df['Loss_score'] > 8) | \n",
    "        (df['Past_Results'] > 1)\n",
    "    ].index\n",
    "    \n",
    "    return idx_df\n",
    "\n",
    "idx_train = get_rules_ids(train)\n",
    "idx_test = get_rules_ids(test)\n",
    "\n",
    "train_orig = train.copy()\n",
    "test_orig = test.copy()\n",
    "\n",
    "train.drop(idx_train, axis=0, inplace=True)\n",
    "test.drop(idx_test, axis=0, inplace=True)\n",
    "\n",
    "train.reset_index(inplace=True)\n",
    "test.reset_index(inplace=True)\n",
    "\n",
    "train.to_csv(\"../data/train_1.csv\", index=False)\n",
    "test.to_csv(\"../data/test_1.csv\", index=False)\n",
    "\n",
    "print(\"New Train Shape : {}\\nNew Test Shape :  {}\".format(train.shape, test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['already_1_indexes_test.pkl']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "llist = test_orig.index.values.tolist()\n",
    "for item in test['index'].values.tolist():\n",
    "    llist.remove(item)\n",
    "\n",
    "print(len(llist))\n",
    "joblib.dump(llist, \"already_1_indexes_test.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
